Towards Robust Informative Path Planning for Spatiotemporal Environments

Abstract

Informative Path Planning (IPP) is an important planning paradigm for various real-world robotic applications such as wildfire monitoring and predicting infection spread in crops. IPP involves planning a path that can learn an accurate belief of the quantity of interest, while adhering to planning constraints. Traditional IPP methods are effective only in static, time-invariant environments and typically require high computation time during execution. This has given rise to reinforcement learning (RL) based IPP methods. However, existing RL-based methods do not address spatiotemporal environments, which present unique challenges due to variations in environment dynamics.

This thesis introduces a robust RL-based IPP framework specifically designed to enable robots to operate effectively across dynamic spatiotemporal environments. Our approach combines domain randomization with our proposed dynamics prediction model (DPM). The DPM constitutes a key contribution of our framework, explicitly modeling how environments evolve over time and extracting latent representations of their specific characteristics. Through extensive evaluations in a wildfire spread prediction task, we demonstrate that our DPM successfully infers environment dynamics online, enabling the RL policy to maintain consistent performance across environments with significantly different dynamics. The results suggest broad applicability to critical monitoring applications where environmental conditions vary considerably.